# Statistics - Assumptions underlying correlation and regression analysis (Never trust summary statistics alone)

The magnitude of a correlation depends upon many factors, including:

• Random and representative sampling
• Measurement of X and Y:
• Several other assumptions:

## Anscombe's quartet

In 1973, statistician Dr. Frank Anscombe developed a classic example to illustrate several of the assumptions underlying correlation and linear regression.

The below scatter-plots have the same correlation coefficient and thus the same regression line.

They have also the same mean and variance.

$$Y = 3 + 0.5 X$$

Only the first one on the upper left satisfies the assumptions underlying a:

## Datasaurus: Never trust summary statistics alone; always visualize your data

The Datasaurus Dozen. While different in appearance, each dataset has the same summary statistics (mean, standard deviation, and Pearson's correlation) to two decimal places.

See:

## Bring your own doodles linear regression

Most of the examples of using linear regression just show a regression line with some dataset. it's much more fun to understand it by drawing data in. Bring your own doodles linear regression

## How to

### test the assumptions in a regression analysis ?

To test the assumptions in a regression analysis, we look a those residual as a function of the X productive variable. (X remaining on the X axis and the residuals coming on the Y axis).

For each of the individual, the residual can be calculated as the difference between the predicted score and a actual score.

If the assumptions are good, there must be:

• no relationship between X and the residual. They must be independent. The relation coefficient must be zero.
• some of the points above zero and some of them below zero. It will indicate Homoscedasticity

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